<![CDATA[Accurate data on agricultural parcel boundaries are essential to support efficient and equitable agrarian management. Conventional methods such as terrestrial surveys and manual digitization are often costly, time-consuming, and inconsistent. Advances in artificial intelligence-based segmentation models, particularly the Segment Anything Model for Geospatial (SAM-Geo), offer new opportunities to accelerate the automatic delineation of agricultural land parcels. This study aims to evaluate the performance of SAM-Geo in extracting agricultural parcel boundaries from 3.98 cm resolution orthophotos in Sumberrahayu Village, Moyudan Subdistrict, Sleman Regency, Yogyakarta Special Region, Indonesia. The research process includes orthophoto preprocessing, SAM-Geo implementation, mask filtering, and accuracy assessment using both area-based metrics (precision, recall, F1-score, and IoU) and boundary-based metrics (boundary precision, recall, and F1-score) with a 1 m buffer tolerance. The results indicate that SAM-Geo can produce highly precise boundary delineation in homogeneous areas, achieving F1-score and IoU values above 96%, while performance declines in heterogeneous areas due to complex land cover conditions. Overall, this study provides one of the first empirical evaluations of SAM-Geo in agricultural landscapes in Indonesia and highlights its potential as an effective approach for agricultural parcel boundary mapping. Ketersediaan data batas bidang tanah pertanian yang akurat menjadi prasyarat penting dalam mendukung pengelolaan agraria yang efisien dan berkeadilan. Metode konvensional seperti survei terestris dan digitasi manual seringkali memerlukan biaya tinggi, waktu lama, serta menghasilkan ketidakkonsistenan data. Perkembangan model segmentasi berbasis kecerdasan buatan, khususnya Segment Anything Model for Geospatial (SAM-Geo), membuka peluang baru untuk mempercepat delineasi batas bidang tanah pertanian secara otomatis. Penelitian ini bertujuan mengevaluasi kinerja SAM-Geo dalam mengekstraksi batas bidang pertanian dari ortofoto beresolusi 3,98 cm di Kalurahan Sumberrahayu, Kapanewon Moyudan, Kabupaten Sleman, D.I. Yogyakarta. Metode penelitian mencakup pra-pemrosesan ortofoto, penerapan SAM-Geo, mask filtering, serta evaluasi akurasi menggunakan metrik berbasis area (precision, recall, F1-score, and IoU) dan berbasis batas (boundary precision, recall, and F1-score) dengan toleransi buffer 1 m. Hasil penelitian menunjukkan SAM-Geo menghasilkan delineasi batas sangat presisi pada area homogen dengan F1-score dan IoU di atas 96%, sedangkan performa menurun pada area heterogen akibat kompleksitas tutupan lahan. Temuan ini menegaskan potensi SAM-Geo sebagai pendekatan efektif untuk pemetaan batas bidang pertanian di Indonesia.]]>
